Tractor fuel rate modeling and simulation using switching Markov chains on CAN-Bus data

Abstract

Agricultural machinery communication data besides the offered diagnostics functionality are a valuable source for optimizing the efficiency of the operations performed. An important factor is the consumed fuel during these operations, which can be obtained from the tractor’s CAN-Bus (Controller Area Network). Thus, methodologies that can model and simulate in-field fuel rates are increasingly important for machine manufacturers. In this study, fuel rate data during plowing with a mounted reversible moldboard plow were collected by a CAN-Bus data logger. Georeferencing of the data was performed by a low-cost DGNSS (Differential Global Navigation Satellite System) receiver. The data were modeled and simulated using Markov chains that proved capable of also modeling the operating mode switching that takes place during headland turning. Based on the calculated Markov transition probability matrices, 10,000 Monte Carlo simulations were performed to produce different realizations of the examined scenarios. Considering all Monte Carlo simulations, the methodology achieved to predict the total fuel consumption with a mean difference of 0.9% and 3.7% standard deviation, compared to the observed total fuel consumption.